Please wait a minute...
Front. Inform. Technol. Electron. Eng.  2012, Vol. 13 Issue (6): 440-451    DOI: 10.1631/jzus.C1100324
    
Feature detection of triangular meshes via neighbor supporting
Xiao-chao Wang, Jun-jie Cao, Xiu-ping Liu, Bao-jun Li, Xi-quan Shi, Yi-zhen Sun
School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China; State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, Dalian University of Technology, Dalian 116024, China; State Key Laboratory of Structural Analysis for Industrial Equipment, School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian 116024, China; Department of Mathematical Sciences, Delaware State University, Dover, DE 19901, USA
Feature detection of triangular meshes via neighbor supporting
Xiao-chao Wang, Jun-jie Cao, Xiu-ping Liu, Bao-jun Li, Xi-quan Shi, Yi-zhen Sun
School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China; State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, Dalian University of Technology, Dalian 116024, China; State Key Laboratory of Structural Analysis for Industrial Equipment, School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian 116024, China; Department of Mathematical Sciences, Delaware State University, Dover, DE 19901, USA
 全文: PDF 
摘要: We propose a robust method for detecting features on triangular meshes by combining normal tensor voting with neighbor supporting. Our method contains two stages: feature detection and feature refinement. First, the normal tensor voting method is modified to detect the initial features, which may include some pseudo features. Then, at the feature refinement stage, a novel salient measure deriving from the idea of neighbor supporting is developed. Benefiting from the integrated reliable salient measure feature, pseudo features can be effectively discriminated from the initially detected features and removed. Compared to previous methods based on the differential geometric property, the main advantage of our method is that it can detect both sharp and weak features. Numerical experiments show that our algorithm is robust, effective, and can produce more accurate results. We also discuss how detected features are incorporated into applications, such as feature-preserving mesh denoising and hole-filling, and present visually appealing results by integrating feature information.
关键词: Feature detectionNeighbor supportingNormal tensor votingSalient measure    
Abstract: We propose a robust method for detecting features on triangular meshes by combining normal tensor voting with neighbor supporting. Our method contains two stages: feature detection and feature refinement. First, the normal tensor voting method is modified to detect the initial features, which may include some pseudo features. Then, at the feature refinement stage, a novel salient measure deriving from the idea of neighbor supporting is developed. Benefiting from the integrated reliable salient measure feature, pseudo features can be effectively discriminated from the initially detected features and removed. Compared to previous methods based on the differential geometric property, the main advantage of our method is that it can detect both sharp and weak features. Numerical experiments show that our algorithm is robust, effective, and can produce more accurate results. We also discuss how detected features are incorporated into applications, such as feature-preserving mesh denoising and hole-filling, and present visually appealing results by integrating feature information.
Key words: Feature detection    Neighbor supporting    Normal tensor voting    Salient measure
收稿日期: 2011-11-01 出版日期: 2012-06-05
CLC:  TP391.4  
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
Xiao-chao Wang
Jun-jie Cao
Xiu-ping Liu
Bao-jun Li
Xi-quan Shi
Yi-zhen Sun

引用本文:

Xiao-chao Wang, Jun-jie Cao, Xiu-ping Liu, Bao-jun Li, Xi-quan Shi, Yi-zhen Sun. Feature detection of triangular meshes via neighbor supporting. Front. Inform. Technol. Electron. Eng., 2012, 13(6): 440-451.

链接本文:

http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1100324        http://www.zjujournals.com/xueshu/fitee/CN/Y2012/V13/I6/440

[1] Yuan-ping Nie, Yi Han, Jiu-ming Huang, Bo Jiao, Ai-ping Li. 基于注意机制编码解码模型的答案选择方法[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(4): 535-544.
[2] Rong-Feng Zhang , Ting Deng , Gui-Hong Wang , Jing-Lun Shi , Quan-Sheng Guan . 基于可靠特征点分配算法的鲁棒性跟踪框架[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(4): 545-558.
[3] Le-kui Zhou, Si-liang Tang, Jun Xiao, Fei Wu, Yue-ting Zhuang. 基于众包标签数据深度学习的命名实体消歧算法[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 97-106.
[4] Yue-ting Zhuang, Fei Wu, Chun Chen, Yun-he Pan. 挑战与希望:AI2.0时代从大数据到知识[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 3-14.
[5] M. F. Kazemi, M. A. Pourmina, A. H. Mazinan. 图像水印框架的层级-方向分解分析[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(11): 1199-1217.
[6] Guang-hui Song, Xiao-gang Jin, Gen-lang Chen, Yan Nie. 基于两级层次特征学习的图像分类方法[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(9): 897-906.
[7] Jia-yin Song, Wen-long Song, Jian-ping Huang, Liang-kuan Zhu. 基于边界分析的森林冠层半球图像中心点定位与分割[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(8): 741-749.
[8] Gao-li Sang, Hu Chen, Ge Huang, Qi-jun Zhao. 基于稠密多变量标签的“连续”头部姿态估计方法[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(6): 516-526.
[9] Xi-chuan Zhou, Fang Tang, Qin Li, Sheng-dong Hu, Guo-jun Li, Yun-jian Jia, Xin-ke Li, Yu-jie Feng. 基于多维尺度拉普拉斯分析方法的全球流感疫情监测[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(5): 413-421.
[10] Chu-hua Huang, Dong-ming Lu, Chang-yu Diao. 基于多尺度轮廓插值生成准密集时变点云模型序列[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(5): 422-434.
[11] Xiao-hu Ma, Meng Yang, Zhao Zhang. 局部不相关的局部判别嵌入人脸识别算法[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(3): 212-223.
[12] Fu-xiang Lu, Jun Huang. 超越隐主题包模型:针对场景类别识别的空间金字塔匹配[J]. Front. Inform. Technol. Electron. Eng., 2015, 16(10): 817-828.
[13] Yu Liu, Bo Zhu. 带有几何形变的变形图像配准[J]. Front. Inform. Technol. Electron. Eng., 2015, 16(10): 829-837.
[14] Zheng-wei Huang, Wen-tao Xue, Qi-rong Mao. 基于无监督特征学习的语音情感识别方法[J]. Front. Inform. Technol. Electron. Eng., 2015, 16(5): 358-366.
[15] Xun Liu, Yin Zhang, San-yuan Zhang, Ying Wang, Zhong-yan Liang, Xiu-zi Ye. 基于高清监控图像的工程车辆检测算法[J]. Front. Inform. Technol. Electron. Eng., 2015, 16(5): 346-357.